This function allows imputing missing values under the assumption that the distribution of complete values has to be Gaussian in each column.

1 | ```
impute.igcda(tab, tab.imp, conditions, q=0.95)
``` |

`tab` |
A numeric vector or matrix with observed and missing values. |

`tab.imp` |
A matrix where the missing values of |

`conditions` |
A vector of factors indicating the biological condition to which each column (experimental sample) belongs. |

`q` |
A quantile value (see Details). |

The mean and variance of the Gaussian distribution are determined using a linear regression between the quantiles of the observed values `q_{obs}`

and the ones of the standard normal distribution `q_{N(0,1)}`

.

The quantile value is used for determining the minimum of imputed values. This minimum is determined by the minimum observed value in the dataset minus `quant_diff(q)`

where `quant_diff(q)`

corresponds to a quantile value of the differences between the maximum and the minimum of the observed values for all the peptides in the condition. As a result, if `q`

is close to 1, `quant_diff(q)`

represents an extrem value between the maximum and the minimum of the intensity values in a condition for a peptide.

The numeric input matrix with imputed values. The distribution of the intensity values in each of its columns is supposed to be Gaussian.

Quentin Giai Gianetto <quentin2g@yahoo.fr>

1 2 3 4 5 6 7 8 9 | ```
#Simulating data
res.sim=sim.data(nb.pept=2000,nb.miss=600,pi.mcar=0.2,para=10,nb.cond=2,nb.repbio=3,
nb.sample=5,m.c=25,sd.c=2,sd.rb=0.5,sd.r=0.2);
#Imputation of missing values with the slsa algorithm
dat.slsa=impute.slsa(tab=res.sim$dat.obs,conditions=res.sim$condition,repbio=res.sim$repbio);
#Imputation of missing values under a Gaussian assumption
dat.gauss=impute.igcda(tab=result$tab.mod, tab.imp=dat.slsa, conditions=res.sim$conditions);
``` |

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